A neuro-computational account of procrastination behavior

Humans procrastinate despite being aware of potential adverse consequences. Yet, the neuro-computational mechanisms underlying procrastination remain poorly understood. Here, we use fMRI during intertemporal choice to inform a computational model that predicts procrastination behavior in independent tests. Procrastination is assessed in the laboratory as the preference for performing an effortful task on the next day as opposed to immediately, and at home as the delay taken in returning completed administrative forms. These procrastination behaviors are respectively modeled as unitary and repeated decisions to postpone a task until the next time step, based on a net expected value that integrates reward and effort attributes, both discounted with delay. The key feature that is associated with procrastination behavior across individuals (both in-lab and at-home) is the extent to which the expected effort cost (signaled by the dorsomedial prefrontal cortex) is attenuated by the delay before task completion. Thus, procrastination might stem from a cognitive bias that would make doing a task later (compared to now) appear as much less effortful but not much less rewarding.


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MRI-based neuroimaging
Behavioral and fMRI study with quantitative data. Three different cohorts of subjects participated in a pilot Experiment 1 (n = 8), in Experiment 2 with behavioral testing only (n = 16), and in Experiment 2 with fMRI (n = 27). 51 healthy adults participated in the study (30 females, median age = 23 ± 2.5 y). This sample is not representative of the general population. The sample size was chosen to be larger than the sample size used in a previous study from our group that identified option values signals during inter-temporal choice (Lebreton et al., 2009).
For the fMRI study, the sample size was chosen to be larger than the sample size used in a previous study from our group that identified option values signals during inter-temporal choice (Lebreton et al., 2009). For participants who participated in the behavioural testing only, no sample size calculation was performed since behavioural analyses were performed combining participants from the fMRI and the behavioral study.
Tasks presentation and behavioral recordings were programmed with MATLAB using the psychophysics Toolbox (www.psychtoolbox.org). No one was present besides the participant and the researcher. The experimenter was not blind to the study hypothesis.
In the Form-filling home task, participants who never sent the forms back (n = 6) were not included in the analyses regarding this task, since there was no delay to predict in their case.
No participant dropped out/declined participation.
Randomization was not applicable in this study since all participants performed the same cognitive task.
Participants were recruited from the community, and were screened for exclusion criteria: left-handedness, age below 18 or above 40, any history of neurologic or psychiatric illness, regular use of drugs or medication, and contraindications to MRI scanning. All participants were recruited from the undergraduate and postgraduate population of Paris Universities via advertisements posted on the online recruitment system of the French National Center for Scientific Research (CNRS) available at www.risc.cnrs.fr. All volunteers received monetary compensation for their participation in line with our institution policies. There may be a selection bias given that participants who agreed to take part in our experiments are generally young adults and are enrolled in higher education. Although, we do not expect this to have a significant influence on our results, future studies with more representative samples of the population will be required to assess the generalisability of our findings.
The study was approved by the Ethics Committee of the Pitie$ -Salpe& trie" re Hospital (Paris, France).

Event-related
The task consisted of 60 choices per block. Reward, effort, and punishment blocks were repeated twice in fMRI session. Responses were self-paced, and were followed by a 0-2000ms intertrial interval jitter.
In the intertemporal choice tasks, we considered choices as the dependent variables, which were regressed against logistic models including experimental factors: difference in value (or cost) and difference in delay.
The area of acquisition included the whole brain except the cerebellum SPM12 was used for preprocessing fMRI data. Preprocessing consisted of spatial realignment, normalization using the same transformation as structural images, and spatial smoothing using a Gaussian kernel with a full-width at half-maximum (FWHM) of 8 mm.
T1-weighted structural images were also acquired, coregistered with the mean EPI, segmented and normalized to a standard T1 template, and averaged across all participants to allow group-level anatomical localization.

SPM standard T1 template
To correct for motion artifact, subject-specific realignment parameters were modeled as covariates of no interest.

n/a
We used a first GLM to generate SPMs of discounted reward and effort, as follows. All trials of the intertemporal choice tasks were modelled as single events with Dirac delta-functions at the time of deliberation onset. The difference in discounted value between chosen and unchosen rewards, or the difference in discounted cost between chosen and unchosen efforts or punishments, was incorporated as parametric modulation.